Process for evaluating data from textile fabrics

ABSTRACT

A process is disclosed for evaluating data obtained from textile fabrics. In order to devise a process which allows data obtained from textile fabrics to be easily compared, assessed in a differentiated manner as to their significance and evaluated, the data are determined in a section ( 3   a   ,3   b ) of the surface of the fabric, sorted according to at least two parameters ( 13,14 ) and represented in an image ( 12, 30 ) as a function of the parameters.

FIELD OF THE INVENTION

The invention relates to a method for evaluating data determined ontextile fabrics.

BACKGROUND OF THE INVENTION

When producing textile fabrics such as woven fabrics, knitted fabrics,etc., faults which cause the ideally regular and precisely structuredsurface to exhibit irregularities or faults are a frequent occurrence.In terms of extent, faults of this kind may range from being very smalland inconspicuous to very large or, for other reasons, conspicuous andmay reduce the value and the function, e.g. the strength or theappearance of the fabric. The finished fabrics are therefore subjectedto an examination for the purpose of indicating faults in the structure.This may be a visual or a machine examination and often takes place bothbefore dyeing or dressing and also before making up. An increase in thequantity of detected faults is to be expected in particular whencarrying out a machine or automated examination, so that acorrespondingly greater data flow may result.

One disadvantage in this case lies in the fact that, although aconsiderable amount of data is available, these data are likely to causeconfusion and may not just serve to improve the quality of the products.It should also be borne in mind that there are a great many producers oftextile fabrics of all kinds and that each producer and also manycustomers are inclined to define and implement their own qualitycriteria. This means that textile fabrics which are assessed bydifferent individuals or institutions result in assessments which cannoteasily be compared with one another.

SUMMARY OF THE INVENTION

As characterized in the claims, the invention therefore achieves theobject of providing a method by which faults which are determined intextile fabrics can easily be compared with one another and assessed andevaluated as to their significance in a differentiated manner.

This is achieved by determining the data on a swatch of the surface ofthe fabric and sorting this data according to at least two parameters. Aswatch can be understood to be the entire surface under consideration ofa fabric or a section from the surface. A section of this kind may bemoved or changed after a period required for acquiring the data, so thatnew data on other zones or swatches of the fabric are periodicallyobtained. The intensity of a pixel or surface element, a longitudinalcoordinate, a latitudinal coordinate, etc. may be considered as data andtherefore also as parameters, for example. The acquired data on thefaults are then represented in an image as a function of selectedparameters, which in turn may be divided into zones which in themselvesare conceived as homogeneous. If two parameters are selected, the resultis a one-dimensional representation. If three parameters are selected,the resulting image is a two-dimensional representation. The image thenrepresents, for example, a classifying field consisting of individualfields which define a class. The class is characterized by the extent ofthe field, which lies in a plane which is regarded as the location forvalues of two parameters. A further parameter may be displayed bysymbols entered in the field.

The advantages achieved by means of the invention lie in particular inthe fact that it enables a structured and standardized assessment offaults in textile fabrics to be carried out. Thus on the one hand valuesof predetermined parameters for the most varied faults can be indicated,while on the other criteria can be created which help to identify thesignificance or value of the faults and to compare this with the valueof other faults. A large data flow on faults in the fabrics can thusalso be processed to provide accurate information on the faultsoccurring.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is illustrated in detail in the following on the basis ofan example and with reference to the accompanying figures, in which:

FIG. 1 shows a respective swatch of the surface of a textile fabric,

FIG. 2 shows a respective swatch according to FIG. 1 with differentfaults, and

FIGS. 3 to 11 in each case show a classifying field.

DETAILED DESCRIPTION

FIG. 1 shows the same run 1 of a textile fabric three times with a fault2. Information on the position of this fault 2 can be obtained, forexample, via coordinates x and y, on its size via values of the extentin two directions s and k, and on its intensity or deviation, forexample in terms of color, from the surrounding area via a value deltai.

FIG. 2 shows a respective swatch 3 a, 3 b of a textile fabric with agrid 4 and four different faults 5, 6, 7 and 8. The swatch 3 a shows afirst possibility for evaluating the size of the faults 5, 6, 7 and 8and the swatch 3 b a second possibility. For this purpose the grid 4divides the swatches 3 a, 3 b into individual small fields 9, and theoccupancy of these fields by the faults 5–8 is interpreted differentlyin the two swatches 3 a and 3 b, as will be discussed further in thefollowing. However in both cases this means that the extent of thefaults through the number of occupied fields is selected as a parameter.Although—should this be a woven fabric—the faults 5, 6, 7, 8 extend intwo directions, weftwise 10 and warpwise 11, the values of theparameters only indicate that the intensity of the faults 5–8 hasexceeded a threshold value and one of the number of occupied fields 9has a proportional extent. The swatches 3 a, 3 b preferably form atleast one rectangle whose sides extend parallel and perpendicularly toboundaries of the fabric or run 1.

FIG. 3 shows an image 12 with two axes 13, 14, along which values ofparameters are plotted. Here the values along the axis 13 are values forthe length of a fault, for example viewed weftwise in a woven fabric,and those along the axis 14 values for the width of a fault, for exampleviewed warpwise in a woven fabric. Lines 15, 17, 19 and 21 divide thewidth of the faults into five classes, while lines 16, 18, 20 and 22divide the length of the faults into five classes. This results overallin twenty five classes for classifying the faults according to size.Symbols 23–29 are drawn in at a plurality of class boundaries, which areindicated by the lines 15–22, these symbols representing the form of afault as is to be expected on the basis of dimensions according to thesaid lines. Numerical values are also entered in the fields defined bythe lines 15 to 22, these values indicating the number of detectedfaults which fall within the class concerned. For this purpose it isassumed that a class represents a homogeneous zone, i.e. no distinctionis made as to whether or not the values of the parameters lie near upperor lower class boundaries or lines 15–22.

FIG. 4 shows an image 30 with axes and lines defining classes as isalready known from FIG. 3. The axes, lines and symbols have thereforebeen given the same reference numbers. Dots 31, 32, 33, etc. are enteredin the fields, the position of which dots in relation to the axes 13 and14 indicates the size of the fault accurately or in a differentiatedmanner. Each dot therefore corresponds to a fault, and the distributionof the faults or the dots thereof is also an indication of thepredominant type of fault in the fabric. Characters A to E are alsoentered along the axis 13 between the lines 14 to 22 and integralnumbers 1 to 5 along the axis 14 between the lines 13 to 21. Each fieldand therefore each class can therefore be clearly designated by thecombination of a number and a character. FIG. 5 shows an image 34 withaxes and lines defining classes as is already known from FIG. 3. Theaxes, lines and symbols have therefore been given the same referencenumbers. Diagonally ascending numerical values, which indicate theintensity of a fault, are provided in the individual fields, whichcorrespond to fault classes. Here the position of a figure indicates theintensity, while the value of the figure indicates the number of faultswith this intensity. Thus numerical values located in the bottomleft-hand side of a field indicate high intensities and numerical valueslocated in the top right-hand side indicate low intensities. FIG. 6shows an image 35 with axes 36 and 37. Values for the area of a fault,for example in CM2, are plotted along the axis 36 and values for theintensity of a fault in percentages along the axis 37. This image 35 isalso divided into fields or classes by lines 38 to 43. Symbols whichindicate the intensity of the fault through the strength of the colorare drawn in at the intersections of the lines 38–43. Numerical valuesin the fields indicate the number of faults occurring in the classconcerned.

FIG. 7 shows an image 44 with axes 45 and 46. Values for the length of afault, for example in cm, are plotted along the axis 45 and values forthe intensity of a fault, for example in percentages, along the axis 46.This image 44 is also divided into fields or classes by lines 47 to 52.The number of detected faults is indicated by the figures in the fields,as already known from FIG. 3. FIG. 8 shows an image 53 with axes 54 and55. Values for the number of occupied fields 9 according to FIG. 2 areplotted along the axis 54 and values for the intensity of a fault alongthe axis 55. This image 53 is also divided into fields or classes bylines 56 to 61. The number of detected faults is indicated by thefigures in the fields, as already known for FIG. 3.

FIG. 9 shows an image 62 with axes 63 and 64. Values for the length offaults in cm are plotted along the axis 63. The axis 64 is divided intoa plurality of zones 64 a to e, and values for the intensity are givenin percentages in each zone. Each of the zones 64 a to 64 e relates to acertain type of fault, for example the zone 64 a relates to weft faults,the zone 64 b to warp faults, the zone 64 c to surface faults, the zone64 d to edge faults and the zone 64 e to holes. Lines 65 to 76 againdivide the image 62 into fields or classes in which numerical valuesindicate the number of detected faults in the class concerned. Theposition of the numerical value in relation to the zone on the axis 64indicates the intensity of the fault. Several numerical values may thusalso occur in one class. The image 62 thereby illustrates aclassification which is based on different types of fault. Differentknown types of fault may be grouped together as desired. So, forexample, the term “weft faults” is here generally understood to meanfaults which predominantly extend weftwise in a woven fabric. Suchfaults are known under the following terms: join, fell, straighteningpoint, shed, weft bar, lashing-in, slubber, fly, thread breakage,mispick.

FIG. 10 shows an image 80 with an axis 81 which is divided into zones 81a to d. Values for intensities in percentages are given along anotheraxis 82. Lines 83 to 93 divide the image 80 into fields or classes.Values for the number of detected faults can again be entered in thefields or classes. For example, the intensity of weft faults can beentered in zone 81 a, the intensity and size of wrap faults in zone 81b, the intensity or size of holes in zone 81 c, the intensity of edgefaults, etc. in zone 81 d, and the numbers thereof.

FIG. 11 shows an image 94 with axes and lines as already found in images12 and 30 (FIGS. 3 and 4). Here the fields or classes are divided by aboundary 97 into two groups 95 and 96, with the boundary extending alonglines 15, 17, 19 and 16, 18, 20. However it is also possible to define aboundary 98 which also divides the individual fields or classes.

The method according to the invention is carried out as follows: Thetextile fabric is scanned in a manner known per se, for example by acamera, and images for swatches of the surface of the fabric are madeand signals derived therefrom are processed. Using algorithms, which donot constitute the subject matter of this invention, for imageprocessing, faults or unusual features in the images of the surface aredetermined from the derived signals by comparison with predeterminedlimit values, patterns, etc. Thus data on faults in a swatch of thefabric are produced. A swatch of this kind is shown, for example, inFIG. 1 and called a run 1. A fault 2, which is distinguished by variousparameters, can be recognized in this. These parameters are itsposition, which is given by coordinates x and y, its size, which isgiven by the values s and k, and its intensity, which causes the faultto actually stand out from the area surrounding it and which isquantified by a qualitative datum, here called delta i.

Different parameters are significant, according to how the fault issubsequently dealt with. For example, if every fault is to be removed,all that is of interest is its position, possibly also its size. If thefabric is then to be assessed as to where the faults are most numerous,such as at the edge, for example, it is again just the position which isof interest. The data are then sorted according to parameters such aslength and width and accordingly represented in an image.

Should there be a requirement for assessing how the fault appears to theeye or how it influences subsequent processing of the fabric, such asdyeing or dressing, its size is of interest and possibly also itsintensity. Then the parameters according to which the data are sortedare the length s and the width k of the fault, as well as its intensitydelta i.

Just one dimension may be determined from the signals obtained fromimage processing in order to detect the size of a fault, or anevaluation according to FIG. 2 may be undertaken. In this case aninvestigation is carried out to establish how many fields 9 are affectedor at least partly covered by a fault. These fields, as marked in swatch3 a, are counted for each fault and the number is plotted, for example,along the axis 54 in FIG. 8. However it is also possible, as shown forswatch 3 b, to take the fields 9 occupied for each fault and to completethem to an extent such that together they form a rectangle whichencompasses the fault. The fields 9 which are comprised in thisrectangle then have to be counted and plotted.

In order to detect the intensity of a fault, the color or brightness ofthe area surrounding the fault is taken as a starting point and anattempt is made to quantify deviations of the color or brightness moreor less accurately or in a graduated manner, this being expressed by avalue delta i. The devices used for image processing determine thedegree to which this is successful.

In order to represent the size of the fault in an image, its length canbe detected in the swatch in a manner known per se and represented in animage 12, 30 by a value on the axis 13. The width of the fault can berepresented in the same way by a value on the axis 14. Together thesetwo values produce, for example, a dot 33 (FIG. 4). This can be left asa dot or simply treated as a fault in class C2, which would mean thatjust one counting value would then be increased by one for this class.For this purpose it is possible to specify certain fields or classes asacceptable and others as unacceptable beforehand. The position of thefault in image 13, 30 then immediately reveals how the fault is to beassessed. Should values for faults accumulate in individual classes,this will equally provide an indication for assessing the fabric.

The intensity of a fault can be represented according to thepossibilities already presented on the basis of the images 34, 35, 44and 53 (FIGS. 5–8).

As shown in FIG. 1, swatches of the surface from which the data areacquired which form a rectangle are particularly suitable, for thefabrics in question are also already in the form of rectangles, thisbeing a result of the manufacturing process. Then sides of the swatchesshould also lie parallel and perpendicularly to the boundaries of thefabric. However the swatch concerned does not conventionally constitutethe entire surface of the fabric. This applies to swatches 3 a, 3 baccording to FIG. 2, which is an enlarged view of a part of the run 1according to FIG. 1.

The form of a fault, as represented by the symbols 23 to 29 in FIG. 3,may also be directly considered as a parameter. In fact a parameter ofthis kind ultimately consists of two parameters (length and width).However it would also be possible to combine the parameter “form” withthe parameter “intensity”, as known from FIG. 6, and in this way obtainanother combination and therefore another image representation. It thusbecomes obvious that only a few possibilities are indicated here,although these can also be developed according to the invention in anobvious manner by combination, for example by interchanging the axes.

Data can be evaluated and, optionally, the textile fabric processed in adifferentiated manner, according to whether the determined data belongto groups 95 or 96 (FIG. 11), which are separated by a boundary 97, 98.For example, the weighting of the faults in group 96 may be reduced withrespect to the faults in group 95. Or faults of group 96 are onlymarked, for example, at the edge of a cloth run, while faults of group95 are removed, for example by unraveling the woven fabric in the areaaround these faults. Generally speaking, boundaries 97, 98, etc. canform groups of classes or categories of faults which initiate differentactions.

1. A method for representing faults detected on textile fabrics forpurposes of evaluation, comprising the following steps: receiving dataassociated with a plurality of faults detected on a swatch of thesurface of the fabric; sorting the data associated with the plurality ofdetected faults in accordance with at least two parameters, wherein atleast one of said two parameters pertains to the size of the detectedfaults; representing the received and sorted data associated with theplurality of detected faults in an image having at least two dimensions,wherein one of said dimensions corresponds to said one of said twoparameters, and another of said dimensions corresponds to the other ofsaid two parameters.
 2. The method of claim 1, wherein said oneparameter is the length of a detected fault.
 3. The method of claim 2wherein the other of said two parameters is the intensity of a detectedfault.
 4. The method of claim 2 wherein the other of said two parametersis the width of a detected fault.
 5. The method of claim 1 wherein saidone parameter is the area of a detected fault.
 6. The method of claim 5wherein the other of said two parameters is the intensity of a detectedfault.
 7. The method of claim 1 wherein said one parameter is the numberof unit fields in said swatch within which a detected fault is located.8. The method of claim 7 wherein the other of said two parameters is theintensity of a detected fault.
 9. The method of claim 1 wherein saidother dimension is divided into a plurality of zones that arerespectively associated with different types of faults.
 10. The methodof claim 1 wherein each of said two dimensions is divided into aplurality of sections to thereby divide said image into a plurality ofclasses, and the plurality of detected faults are represented asnumerical values within the classes with which they are respectivelyassociated.
 11. The method of claim 10 wherein the position of anumerical value within a class indicates the value of a parameter fordetected faults represented by that number.
 12. The method of claim 11wherein the parameter depicted by the positions of the numerical valuesis a third parameter different from said two parameters.
 13. The methodof claim 12 wherein said third parameter is intensity.
 14. A method forrepresenting faults detected on textile fabrics for purposes ofevaluation, comprising the following steps: receiving data associatedwith a plurality of faults detected on a swatch of the surface of thefabric; sorting the data associated with the plurality of detectedfaults in accordance with at least two parameters, wherein at least oneof said two parameters pertains to the intensity of the detected faults;representing the received and sorted data associated with the pluralityof detected faults in an image having at least two dimensions, whereinone of said dimensions corresponds to said one of said two parameters,and another of said dimensions corresponds to the other of said twoparameters.
 15. The method of claim 14 wherein one of said dimensions isdivided into a plurality of zones that are respectively associated withdifferent types of faults.
 16. The method of claim 14 wherein each ofsaid two dimensions is divided into a plurality of sections to therebydivide said image into a plurality of classes, and the plurality ofdetected faults are represented as numerical values within the classeswith which they are respectively associated.
 17. The method of claim 16wherein the position of a numerical value within a class indicates thevalue of a parameter for detected faults represented by that number.